Multinomial Level-Set Framework for Multi-region Image Segmentation

  • Tammy Riklin RavivEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10302)


We present a simple and elegant level-set framework for multi-region image segmentation. The key idea is based on replacing the traditional regularized Heaviside function with the multinomial logistic regression function, commonly known as Softmax. Segmentation is addressed by solving an optimization problem which considers the image intensities likelihood, a regularizer, based on boundary smoothness, and a pairwise region interactive term, which is naturally derived from the proposed formulation. We demonstrate our method on challenging multi-modal segmentation of MRI scans (4D) of brain tumor patients. Promising results are obtained for image partition into the different healthy brain tissues and the malignant regions.


Multinomial Logistic Regression Brain Tumor Patient Tumor Segmentation Healthy Brain Tissue Dice Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Electrical and Computer Engineering and the Zlotowski Center for NeuroscienceBen-Gurion University of the NegevBeer-ShevaIsrael

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